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NEW QUESTION # 29
When configuring an out-of-core HPL burn-in for a 40B matrix on 8x H100 nodes, which environment variable prevents GPU out-of-memory errors while reserving space for drivers?
Answer: B
Explanation:
The correct option is export HPL_OOC_SAFE_SIZE=4.0. NVIDIA HPL out-of-core mode allows matrix data that exceeds GPU memory capacity to be placed in host memory, but the GPU still needs reserved memory for drivers and runtime overhead. NVIDIA documents HPL_OOC_SAFE_SIZE as the amount of GPU memory, in GiB, reserved for the driver and not used by HPL out-of-core mode; increasing it is recommended when GPU out-of-memory errors occur. HPL_OOC_MODE=0 disables out-of-core mode, which would not help run a larger 40B matrix. HPL_OOC_NUM_STREAMS=8 changes the number of CUDA streams used for out-of-core operations, but it does not reserve driver memory.
HPL_OOC_MAX_GPU_MEM=90 limits total GPU memory use, but the specific variable intended to leave safe driver space is HPL_OOC_SAFE_SIZE. During cluster burn-in, this setting helps preserve test validity while avoiding false failures caused by memory reservation issues rather than actual hardware instability.
NEW QUESTION # 30
You have an Intel Xeon Gold server with 2 NVIDIA Tesla VI 00 GPUs. After deploying your A1 application, you observe that one GPU is consistently running at a significantly higher temperature than the other What could be a plausible reason for this behavior?
Answer: C,E
Explanation:
Uneven heat distribution often points to airflow problems or unbalanced workloads. Inadequate airflow can cause localized hotspots. Uneven workload distribution will naturally cause one GPU to work harder and generate more heat. While a defective GPU or driver issues are possibilities, they are less likely than airflow and workload imbalances in this scenario. High ambient temperature is also a contributing factor but less direct.
NEW QUESTION # 31
You are tasked with updating both NVIDIA GPU drivers and DOCA drivers on a set of servers used for AI workloads. The environment previously had an older driver stack and custom kernel modules. What is the most important step to successfully upgrade the drivers without causing conflicts?
Answer: C
Explanation:
Removing the existing GPU, DOCA, OFED, and related kernel-module stack before installing the new versions prevents version mismatches, stale kernel modules, and dependency conflicts. This is especially important when the servers previously used an older stack with custom modules.
NEW QUESTION # 32
You want to limit the GPU memory available to a specific Docker container running a deep learning model. Which of the following 'docker run' commands using the NVIDIA Container Toolkit is the most appropriate?
Answer: E
Explanation:
The correct syntax to limit GPU memory for a Docker container using the NVIDIA Container Toolkit involves specifying the GPU device using its UUID and then setting the memory limit. 'docker run --gpus '"device=GPU-UUlD:memory=4g"' my-image' is the correct way to achieve this. The others are syntactically incorrect or do not utilize the intended functionality. You need to find the GPU UUID using 'nvidia-smi' and replace 'GPIJ-IJIJID' with it.
NEW QUESTION # 33
A team is installing the NVIDIA Run:ai control plane on a Kubernetes cluster. Which two (2) options are most critical to validate before proceeding? (Pick the 2 correct responses below)
Answer: A,B
Explanation:
NVIDIA Run:ai is an advanced orchestration platform designed to optimize GPU resource allocation within Kubernetes environments. Because Run:ai is cloud-native, its control plane and worker agents are deployed as Kubernetes resources. Therefore, the absolute first prerequisite is arunning Kubernetes cluster(Option B) to host the services. Secondly, Run:ai utilizesHelm, the package manager for Kubernetes, to manage its complex installation charts, deployments, and service configurations. Without Helm installed on the administrative machine (Option A), the installation scripts will fail to execute. While having GPUs (Option C) is the ultimate goal for the worker nodes, thecontrol planeitself can be installed on a cluster before all GPU hardware is physically present. Disabling NTP (Option D) is never recommended; in fact, accurate time synchronization is vital for the TLS certificates and logging used by Run:ai and Kubernetes.
NEW QUESTION # 34
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